Objective Bayesian analysis for the generalized exponential distribution

TL;DR

This paper develops an objective Bayesian framework for the generalized exponential distribution, introducing a Jeffreys prior, validating posterior propriety, and proposing an efficient sampling method, with simulation and real-data applications.

Contribution

It presents a novel objective Bayesian inference approach for the generalized exponential distribution, including prior validation and a new sampling algorithm.

Findings

Posterior distribution is proper under structured priors.

The proposed sampling algorithm is efficient for posterior inference.

Simulation studies demonstrate good finite-sample performance.

Abstract

In this paper, we consider objective Bayesian inference of the generalized exponential distribution using the independence Jeffreys prior and validate the propriety of the posterior distribution under a family of structured priors. We propose an efficient sampling algorithm via the generalized ratio-of-uniforms method to draw samples for making posterior inference. We carry out simulation studies to assess the finite-sample performance of the proposed Bayesian approach. Finally, a real-data application is provided for illustrative purposes.